Adaptive Frequent Pattern Algorithm using CAWFP-Tree based on RHadoop Platform

RHadoop 플랫폼기반 CAWFP-Tree를 이용한 적응 빈발 패턴 알고리즘

  • Park, In-Kyu (Dept. of Game Software, College of Engineering Joongbu University)
  • 박인규 (중부대학교 게임소프트웨어학과)
  • Received : 2017.05.01
  • Accepted : 2017.06.20
  • Published : 2017.06.28


An efficient frequent pattern algorithm is essential for mining association rules as well as many other mining tasks for convergence with its application spread over a very broad spectrum. Models for mining pattern have been proposed using a FP-tree for storing compressed information about frequent patterns. In this paper, we propose a centroid frequent pattern growth algorithm which we called "CAWFP-Growth" that enhances he FP-Growth algorithm by making the center of weights and frequencies for the itemsets. Because the conventional constraint of maximum weighted support is not necessary to maintain the downward closure property, it is more likely to reduce the search time and the information loss of the frequent patterns. The experimental results show that the proposed algorithm achieves better performance than other algorithms without scarifying the accuracy and increasing the processing time via the centroid of the items. The MapReduce framework model is provided to handle large amounts of data via a pseudo-distributed computing environment. In addition, the modeling of the proposed algorithm is required in the fully distributed mode.


Data Mining;Weight Frequent Pattern;Centroid;Downward Closure;MapReduce


Supported by : 중부대학교


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